ai_embed

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must disclose behavioral traits. It mentions a cost of 2 sats per call, which is helpful, but does not explain how authentication works via preimage/macaroon parameters, rate limits, or behavior when both text and texts are provided.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise (one sentence plus cost note) and front-loads the key information. Every word is meaningful, with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, and the description does not specify the return format (e.g., array of floats). Given the complexity of the tool (4 parameters, payment mechanism), the description lacks completeness for proper agent invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 75%, which is high. The description adds a small summary ('single text or batch') that overlaps with existing parameter descriptions but does not provide additional meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool produces a 768-dim embedding vector for RAG, handling single text or batch input. It uses specific terminology and distinguishes from sibling AI tools (no other embedding tool).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions use for RAG but does not provide explicit when-to-use or when-not-to-use guidance. Among siblings, it is the only embedding tool, so selection is implicit, but prerequisites like payment (L402) are not addressed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.